Thermal Stability and Kinetic Study on Thermal Degradation of Vitamin D3 in Fortified Canola Oil.

Nowadays, fortified vegetable oils with vitamin D3 are widely available in different countries and are consumed daily. The reduction rate of added vitamin D3 in fortified canola oil during heating process, the changes in oxidative status, and the thermal kinetic degradation of vitamin D3 in the fortified oil were investigated. For this purpose, canola oil was fortified at two levels of vitamin D3 with 5.625 µg/mL (low concentration or LC) and 13.585 µg/mL (a high concentration or HC). Samples were heated isothermally at 100, 150, and 180 °C for 30 min. The vitamin D3 concentration was determined by the high-performance liquid chromatographic method. The retention of vitamin D3 in samples treated at 100 °C for 30 min showed no significant reduction. Samples treated at 150 and 180 °C depending on the initial concentration showed the retention of 67.5% to 72.97% and 33.16% to 40.35% of vitamin D3 , respectively. An inverse relationship was found between the increment of lipid oxidation products (peroxide and anisidine values) and the retention of vitamin D3 . Kinetic parameters such as rate constant, activation energy, decimal reduction time, and quotient indicator were also calculated. An Arrhenius relationship was used for the assessment of temperature dependence of vitamin D3 degradation. Activation energies for vitamin D3 in LC and HC between 100 and 180 °C were found to be 44.01 and 38.77 kJ/mol, respectively. PRACTICAL APPLICATION: The oil can be fortified with vitamin D3 at low cost and offers a good bioavailability. A high-temperature cooking method may not be appropriate for the fortified products containing high lipid content.

Automatic segmentation of vertebrae in 3D CT images using adaptive fast 3D pulse coupled neural networks.

Two systems are presented for segmentation of vertebrae in a 3D computed tomography (CT) image. The first method extracts seven features from each voxel and uses a multi-layer perceptron neural network (MLPNN) to classify the voxel as vertebrae or background. In the second method, the segmentation is completed in two steps: first, a newly developed adaptive pulse coupled neural network (APCNN) directly applied to a given image segments vertebrae, then the result is refined using a median filter. In the developed APCNN, the values for the user-defined parameters of the pulse coupled neural networks (PCNN) are adaptively adjusted for each image individually, instead of using one value for all images as in conventional PCNN. The performance of both systems in terms of Dice index (DI) was evaluated and compared against the state-of-the-art segmentation methods using seventeen clinical and standard CT images. Overall, both systems demonstrated statistically similar and promising performance with average DI > 95%. Compared to existing PCNN-based segmentation algorithms, the accuracy of the proposed APCNN improved by 29.3% on average. The developed APCNN-based system is more accurate than MLPNN-based system and existing PCNN-based algorithms in segmentation of vertebrae with blurred and weak boundaries and in the images contaminated by salt- and- pepper noise. In terms of computation time, the APCNN-based system is 16 times faster than the MLPNN-based system. Consequently, the presented APCNN-based algorithm is both accurate and fast and could be used in clinical environment for segmentation of vertebrae in 3D CT images.